Network Working Group J. Dong
Internet-Draft Huawei Technologies
Intended status: Informational D. Li
Expires: 4 September 2025 Tsinghua University
Q. Shi
Huawei Technologies
P. Huo
ByteDance
3 March 2025
Current State of the Art for Routing in AI Networks
draft-dong-fantel-state-of-art-01
Abstract
This document provides an overview of routing technologies that
address the needs of traffic engineering and load balancing, with a
focus on fast notification for example in adaptive or perceptive
routing. As the scale and complexity of networks grow, these
technologies are becoming increasingly important when fault tolerance
and rapid convergence are critical. This document explores existing
solutions from both the IETF and the broader industry, highlighting
their applicability to various use cases, including AI workloads and
general services that demand low-latency, fault recovery, and dynamic
load distribution across data center networks and inter data center.
It also offers suggestions for potential IETF initiatives to further
develop and standardize these techniques.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 4 September 2025.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Proposals in IETF . . . . . . . . . . . . . . . . . . . . . . 3
2.1. Gap Analysis, Problem Statement and Requirements . . . . 3
2.2. Framework . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3. Information Model . . . . . . . . . . . . . . . . . . . . 5
2.4. Solutions . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4.1. Topology-specific Routing Mechanisms . . . . . . . . 5
2.4.2. Extensions to Routing Protocols . . . . . . . . . . . 6
2.4.3. New Protocols for Fast Notification . . . . . . . . . 6
3. Implementations in Industry . . . . . . . . . . . . . . . . . 7
3.1. DLB and GLB . . . . . . . . . . . . . . . . . . . . . . . 7
3.2. VRF-based Adaptive Routing . . . . . . . . . . . . . . . 7
3.3. CONGA . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.4. Centralized TE and E-ECMP . . . . . . . . . . . . . . . . 8
4. Summary and Potential Work . . . . . . . . . . . . . . . . . 8
5. Security Considerations . . . . . . . . . . . . . . . . . . . 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
7. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 9
8. Informative References . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction
The emergence of new applications, like AI applications brings new
requirements to networks, such as load balancing and network
reliability. AI-driven applications tend to generate highly dynamic
and unpredictable traffic patterns, and require high performance in
terms of throughput, latency and packet loss. As a result, there is
a growing need for Adaptive and Perceptive Routing mechanisms that
can respond to these new demands. As widely discussed both in
standards and the industry, Adaptive/Perceptive Routing allows
networks to make real-time adjustments in response to varying traffic
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loads and network conditions, ensuring that network resources are
optimally utilized and congestion is minimized.
This document provides an overview of routing technologies that
address the needs of traffic engineering and load balancing, with a
focus on the fast notification, for example in adaptive routing where
the routing decision adapts to network events. As the scale and
complexity of networks grow, these technologies are becoming
increasingly important when fault tolerance and rapid convergence are
critical. This document explores existing solutions from both the
IETF and the broader industry, highlighting their applicability to
various use cases, including AI workloads and general services that
demand low-latency fault recovery and dynamic load distribution
across data center networks and inter data center. It also offers
suggestions for potential IETF initiatives to further develop and
standardize these techniques.
2. Proposals in IETF
There are several individual drafts in the IETF which describe the
problems, gaps, requirements and potential frameworks for routing in
AI networks. This section briefly goes through these documents,
summarizes the current state of this topic in the IETF, and
identifies the open issues which needs further work.
2.1. Gap Analysis, Problem Statement and Requirements
[I-D.hcl-rtgwg-ai-network-problem] analyzes the gaps in the networks
used for AI training, and describes the requirements for
improvements. It firstly introduces the charateristics of AI
training traffic, then focuses on the gaps and requiements in several
key technologies: Load Balancing, Congestion Control and Fast
Failover. It is not clear whether the congestion control mentioned
in this document is more related to the network layer or the
transport layer.
[I-D.cheng-rtgwg-ai-network-reliability-problem] fucuses on the
reliability problem and requirements in AI networks. It describes
the existing mechanisms for network reliability, including link fault
detection, ECMP, fast reroute and fast route convergence, (e.g. BGP
Prefix Independent Convergence (PIC)), then analyzes the gaps in the
timing of fault detection, notification propagation and switchover.
In the end, the draft lists a set of requirements for new techniques
on fault detection, congestion elimination, fast fault notification
and fast switching over.
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[I-D.wang-rtgwg-dragonfly-routing-problem] introduces the
characteristics and routing mechanisms of dragonfly topology,
including Minimal Routing, Non-Minimal Routing, Adaptive Routing and
Valiant Load-Balanced Routing. Then it analyzes the gaps of existing
routing mechanism in dragonfly networks, such as load balancing and
adaptive routing notification, in the end the drafts list the
requirements on routing protocols for dragonfly networks.
The analysis shows that there are some overlaps in the gap analysis
and problem statement between these documents. The common problems
and gaps identified for routing in AI networks are load balancing and
fast failure notification. The requirements on routing protocols and
the notification mechanism need further investigation.
2.2. Framework
[I-D.cheng-rtgwg-adaptive-routing-framework] describes a framework
for adaptive routing, including a set of components, their
interaction and the workflow. It identifies the problems with
existing flow-based load balancing in AI networks, especially when
congestion happens on some of the links. The solutions are
classified into two types: flow-based adjustments and packet-based
adjustments. The flow-based ajdustments are further categorized into
weight-based dyanamic ECMP and Flow redirection. The overall
adaptive routing framework consists of routing plane, forwarding
plane, adaptive routing policy and the remote congestion detection.
In the forwarding plane, it proposes to add remote path info to the
forwarding table, and the quality of the links can be updated in
response to congestion, then new weight value can be calculated to
optimize the weight-based load balancing. In the routing plane, the
draft analyzes the possible extensions needed in routing protocols
for obtaining the path information. In congestion detection, it
gives the definition of congestion, the general mechanisms for
detecting congestion, then describes the types of information needs
to be carried in the congestion notification message. It also
anlalyzed the options of transmitting congestion information, either
by extending existing protocols or introducing new protocols.
[I-D.liu-rtgwg-path-aware-remote-protection] desribes the framework
of path-aware remote protection. It contains the routing plane, the
forwarding plane and the remote failure notification. Similar to
[I-D.cheng-rtgwg-adaptive-routing-framework], path awareness is
required in the routing plane and forwarding plane for rapid
switchover. It gives the requirements on remote link detection that
the failure notification should be indepedent of routing protocols,
and broadcast flooding should be avoided. It also talks about the
protection scope of remote protection, which may have impacts on the
speed and propagation of failure notification.
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[I-D.li-rtgwg-distributed-lossless-framework] analyzes the challenges
in building ultra large scale data centers for AI training, and
introduces the scenarios of distributed AIDC networks. Then it
proposes a framework and a set of key technologies for building
lossless and reliable interconnection between multiple data centers.
Global load balancing, precise flow-control and packet loss detection
are mentioned as key mechanisms.
This shows that the scope of the framework documents are different,
while some of the content are overlapped. There is a possibility to
combine the existing framework documents to build a complete
framework which includes both congestion and protection, and covers
both intra-DC and inter-DC scenarios.
2.3. Information Model
[I-D.zhou-rtgwg-perceptive-routing-information] defines the
information model for perceptive routing (PR), which provides the
necessary information and relationship of the components in the
implementation of adaptive routing systems. It offers a common
information model for representing the state of the network, allowing
devices to communicate critical information such as failures,
congestion, and optimal paths, facilitating dynamic and automated
decision-making. The information model for a PR sensing node
includes a set of local information and network-level information
which can be used to evaluate whether a PR notification needs to be
generated and sent. The information model for a PR routing node
includes a set of decisions and behaviors to be made by a PR routing
node on receipt of the PR notification.
2.4. Solutions
In the solution space, there are several documents which propose the
mechanisms for routing in AI networks include topology-specific
mechanisms, extensions to routing protocols and the new protocols for
the notification of network status.
2.4.1. Topology-specific Routing Mechanisms
[I-D.agt-rtgwg-dragonfly-routing] provides on overview of the
Dragonfly+ topoloy, and describes the routing and forwarding
mechanisms in the Dragonfly+ topology, which relies heavily on non-
minimal routing and adaptive load balancing for efficient use of
available network capacity. It uses existing routing mechanisms such
as VRF, route leaking and EBGP to achieve route propagation control
and routing policy. In terms of adaptive load balancing, the purpose
is to fill paths starting from high priority, and try to move flows
from congested paths as a reaction to congestion. It requires that
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adaptive load balancing be able to work without complete knowledge of
network link utilization and queue state. It also considers that
adaptive routing can work as a complementary failure handling
mechanism faster than routing convergence. While the detailed
adaptive routing and load balancing mechanisms are left to other
documents.
2.4.2. Extensions to Routing Protocols
[I-D.xu-idr-fare] proposes extensions to BGP to carry end-to-end path
bandwidth within the data center fabric for adaptive routing. In the
draft a new type of BGP Extended Community is defined, and its usage
in BGP route update distribution is specified using examples of
3-stage and 5-stage Clos networks. With the information of path
bandwidth and link bandwidth, weighted ECMP load balancing can be
performed.
[I-D.wang-idr-next-next-hop-nodes] proposes extensions to BGP to
carry the next-next hop nodes associated with a given BGP next hop.
One usage of the next-next hops information is for global load
balancing (GLB) in a Clos network, where load balancing based on
local next-hop information cannot mitigate the congestion, and it
requires help from the previous hop(s) to shift the traffic to
alternative next-hop nodes towards a next-next hop node. The next-
next hop information is encoded as a new characteristic code of the
BGP Next Hop Dependent Characteristics Attribute.
2.4.3. New Protocols for Fast Notification
[I-D.wh-rtgwg-adaptive-routing-arn] specifies Adaptive Routing
Notification (ARN) as a general mechanism to proactively disseminate
congestion/failure detection and elimination information for remote
nodes to perform re-routing policies. An ARN message contains two
kinds of information: information reflecting the type of notification
(congestion or failure) and quantifiable metrics (e.g., congestion
level), and information carrying details about the affected object
(e.g., affected traffic, affected paths). The ARN messages can be
sent using unicast or multicast to other network nodes. The format
of the ARN packets and its processing on the sending and receiving
nodes are also specified. The impact to route ocillation and packet
reordering caused by ARN are for further study.
[I-D.liu-rtgwg-adaptive-routing-notification] describes the
information carried in Adaptive Routing Notification (ARN) messages
and the mechanisms of delivering ARN message in the network. The
draft gives three options, each of which specifies the information
carried in the ARN message and the mechanism of sending the message
to specific network nodes. The complexity and overhead in
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implementation are also analyzed. It also introduces an ARN TAG
mechanism to control the enabling of ARN meschanism on specific
traffic flows.
[I-D.zzhang-rtgwg-router-info] specifies a generic mechanism for a
router to advertise some information to its neighbors. One use case
is to advertise link or path information to allow receiving node to
better react to network changs . The draft firstly analyzes the
requirements for the information advertisement, then chooses to use
UDP as a better choice comparing to IGP. The format of the message
and the contained information are defined in the draft. How the IP
address of the target nodes are obtained, and the processing on the
receiving nodes are considered out of scope of the draft.
3. Implementations in Industry
One of the most prominent applications of fast notification is
adaptive routing, which has recently gained significant traction in
Ethernet-based Artificial Intelligence Data Centers (AIDCs). These
data centers require real-time network information to dynamically
handle the unpredictable and bursty traffic of AI/ML applications.
The following sections highlight some notable implementations of
adaptive routing in modern data center environments.
3.1. DLB and GLB
Dynamic Load Balancing (DLB) is a mechanism that selects the next hop
for packets based on the quality of the local switch port or other
local information. Global Load Balancing (GLB) extends this approach
by considering the quality of downstream paths when selecting the
next hop, thereby optimizing traffic distribution and improving
overall network efficiency. The DLB and GLB mechanisms are
implemented by many data center switches, including those from
Broadcom [GLB-Broadcom], Juniper [GLB-Juniper], and Nvidia
[GLB-NVIDIA].
3.2. VRF-based Adaptive Routing
Huawei's CloudEngine series switches implement adaptive routing
through a VRF-based architecture [VRF-AR]. This design maintains
three distinct routing tables on each device: one for shortest paths,
one for non-shortest paths, and a combined table for both. Path
selection is dynamically adjusted based on real-time network
conditions, including both the local port status and global
congestion status. The latter is communicated via Adaptive Routing
Notifications (ARN), allowing for intelligent, congestion-aware
routing decisions that enhance overall network performance and
resiliency.
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3.3. CONGA
Cisco developed a solution [CONGA] for datacenter fatrics. CONGA is
a network-based, distributed, congestion-aware load balancing
mechanism designed for datacenter Clos topologies and network
virtualization overlays. It is CONGA splits TCP flows into flowlets,
estimates real-time congestion on fabric paths using feedback from
remote switches, and dynamically allocates flowlets to optimal paths.
3.4. Centralized TE and E-ECMP
Meta has developed several solutions such as centralized Traffic
Engineering (TE) and Enhaneced ECMP (E-ECMP) which are specifically
designed for AI workloads [TE-EECMP].
In the centralized TE approach, real-time workload and network
topology information are collected and transmitted to the control
plane. The TE engine then executes the Constrained Shortest Path
First (CSPF) algorithm to generate optimized flow placements every 30
seconds. The resulting flow placement policy overrides the default
BGP routes on each switch, with BGP routing decisions serving
exclusively as a backup mechanism.
E-ECMP is designed to address the low entropy inherent in AI workload
flows. To achieve this, switches are configured to additionally hash
the QP field of RoCE packets. Furthermore, NIC-to-NIC flows are
divided into multiple flows to increase the number of QPs, thereby
enhancing load distribution.
4. Summary and Potential Work
The analysis about the current state of the art for routing in AI
networks shows that "Adaptive Routing" is a vague term and has
different meanings in different documents or implementations. In
some cases, it refers to dynamic load balancing taking the link
congestion status into consideration. While in some other cases, it
refers to fast switchover due to network failure. As claimed in some
documents, adaptive routing is faster than route convergence, the
fuctionalities specified in the documents are not directly related to
routing or path computation. In the industry, global load balancing
(GLB) is used in many solutions, while it does not cover the failure
cases. It seems that a better term may be needed in IETF to more
accurately reflect the functionality.
According to the framework and solutions documents, it seems the
related work mainly includes: routing extensions for more visibility
in network topology and capacity information, fast notification of
network congestion or failure conditions, and dynamic traffic
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engineering and load balancing mechanisms. In some gap analysis and
problem statements, congestion control is also considered as one of
the problems to be solved. While since congestion managment belongs
to the WIT area in IETF, it is not clear whether it can be pursued
together with other functions in the RTG area.
In many of the analyzed documents, it is assumed that the underlay
routing is based on EBGP, and extensions to BGP for the advertisement
of additional network information are proposed. Whether other
routing protocol options (e.g., IGP, IBGP, BGP-SPF, RIFT etc.) also
need to be investigated is something for further consideration.
In terms of load balancing, currently most of the documents and
solutions focus on the load balancing over ECMP paths, while in some
topologies (such as Dragonfly and Dragonfly+), non-ECMP paths may
also need to be taken into consideration.
It seems the there is common interest in the fast notification
mechanism for traffic engineering and load balancing. This may be
something a new initiative in IETF could start with, and there is
some open questions for further discussion. As mentioned in some of
the documents, congestion notification is required for dynamic load
balancing or flow redirect, and failure notification is required for
fast switchover. Currently it is not clear whether it is possible to
provide a general mechanism for the notification of both the
congestion and failure conditions, or there is enough differences
between the two cases that separate mechanisms are needed. Moreover,
further investigation is needed on whether a new protocol is needed
for fast notification, or extensions based on existing protocols
would also meet some of the requirements.
5. Security Considerations
TBD
6. IANA Considerations
There are no requested IANA actions.
7. Acknowledgments
The authors would like to thank Xuesong Geng and Hang Shi for their
review and discussion of this document.
8. Informative References
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[GLB-Broadcom]
"Cognitive routing in the Tomahawk 5 data center switch",
n.d., <https://www.broadcom.com/blog/cognitive-routing-in-
the-tomahawk-5-data-center-switch>.
[GLB-Juniper]
"Global Load Balancing (GLB)", n.d.,
<https://www.juniper.net/documentation/us/en/software/
junos/ai-ml-evo/topics/topic-map/glb.html>.
[GLB-NVIDIA]
"Turbocharging Generative AI Workloads with NVIDIA
Spectrum-X Networking Platform", n.d.,
<https://developer.nvidia.com/blog/turbocharging-ai-
workloads-with-nvidia-spectrum-x-networking-platform>.
[VRF-AR] "What Is Dragonfly Adaptive Routing?", n.d.,
<https://info.support.huawei.com/info-
finder/encyclopedia/en/Dragonfly+Adaptive+Routing.html>.
[CONGA] "CONGA-Distributed Congestion-Aware Load Balancing for
Datacenters", n.d.,
<https://dl.acm.org/doi/pdf/10.1145/2740070.2626316>.
[TE-EECMP] "RDMA over Ethernet for Distributed Training at Meta
Scale", n.d.,
<https://dl.acm.org/doi/10.1145/3651890.3672233>.
[I-D.hcl-rtgwg-ai-network-problem]
Huo, P., Chen, G., Lin, C., and Z. Jiang, "Gap Analysis,
Problem Statement, and Requirements in AI Networks", Work
in Progress, Internet-Draft, draft-hcl-rtgwg-ai-network-
problem-01, 23 August 2024,
<https://datatracker.ietf.org/doc/html/draft-hcl-rtgwg-ai-
network-problem-01>.
[I-D.cheng-rtgwg-ai-network-reliability-problem]
Cheng, W., Lin, C., wangwenxuan, and B. Xu, "Reliability
in AI Networks Gap Analysis, Problem Statement, and
Requirements", Work in Progress, Internet-Draft, draft-
cheng-rtgwg-ai-network-reliability-problem-02, 3 November
2024, <https://datatracker.ietf.org/doc/html/draft-cheng-
rtgwg-ai-network-reliability-problem-02>.
[I-D.wang-rtgwg-dragonfly-routing-problem]
Wang, R., Lin, C., wangwenxuan, and W. Cheng, "Routing
mechanism in Dragonfly Networks Gap Analysis, Problem
Statement, and Requirements", Work in Progress, Internet-
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Draft, draft-wang-rtgwg-dragonfly-routing-problem-02, 4
September 2024, <https://datatracker.ietf.org/doc/html/
draft-wang-rtgwg-dragonfly-routing-problem-02>.
[I-D.cheng-rtgwg-adaptive-routing-framework]
Cheng, W., Lin, C., Wang, K., Ye, J., Zhuang, R., and P.
Huo, "Adaptive Routing Framework", Work in Progress,
Internet-Draft, draft-cheng-rtgwg-adaptive-routing-
framework-03, 20 October 2024,
<https://datatracker.ietf.org/doc/html/draft-cheng-rtgwg-
adaptive-routing-framework-03>.
[I-D.liu-rtgwg-path-aware-remote-protection]
Liu, Y., Lin, C., Chen, M., Zhang, Z., Wang, K., and Z.
He, "Path-aware Remote Protection Framework", Work in
Progress, Internet-Draft, draft-liu-rtgwg-path-aware-
remote-protection-02, 13 September 2024,
<https://datatracker.ietf.org/doc/html/draft-liu-rtgwg-
path-aware-remote-protection-02>.
[I-D.li-rtgwg-distributed-lossless-framework]
Li, C., Ji, S., and K. Zhu, "Framework of Distributed AIDC
Network", Work in Progress, Internet-Draft, draft-li-
rtgwg-distributed-lossless-framework-00, 21 October 2024,
<https://datatracker.ietf.org/doc/html/draft-li-rtgwg-
distributed-lossless-framework-00>.
[I-D.zhou-rtgwg-perceptive-routing-information]
Zhou, T., Li, D., and X. Geng, "Perceptive Routing
Information Model", Work in Progress, Internet-Draft,
draft-zhou-rtgwg-perceptive-routing-information-00, 18
October 2024, <https://datatracker.ietf.org/doc/html/
draft-zhou-rtgwg-perceptive-routing-information-00>.
[I-D.agt-rtgwg-dragonfly-routing]
Afanasiev, D., Roman, and J. Tantsura, "Routing in
Dragonfly+ Topologies", Work in Progress, Internet-Draft,
draft-agt-rtgwg-dragonfly-routing-01, 4 March 2024,
<https://datatracker.ietf.org/doc/html/draft-agt-rtgwg-
dragonfly-routing-01>.
[I-D.xu-idr-fare]
Xu, X., Hegde, S., He, Z., Wang, J., Huang, H., Zhang, Q.,
Wu, H., Liu, Y., Xia, Y., Wang, P., and Tiezheng, "Fully
Adaptive Routing Ethernet using BGP", Work in Progress,
Internet-Draft, draft-xu-idr-fare-02, 1 September 2024,
<https://datatracker.ietf.org/doc/html/draft-xu-idr-fare-
02>.
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[I-D.wang-idr-next-next-hop-nodes]
Wang, K., Haas, J., Lin, C., and J. Tantsura, "BGP Next-
next Hop Nodes", Work in Progress, Internet-Draft, draft-
wang-idr-next-next-hop-nodes-02, 2 December 2024,
<https://datatracker.ietf.org/doc/html/draft-wang-idr-
next-next-hop-nodes-02>.
[I-D.wh-rtgwg-adaptive-routing-arn]
Wang, H., Huang, H., Geng, X., Xu, X., and Y. Xia,
"Adaptive Routing Notification", Work in Progress,
Internet-Draft, draft-wh-rtgwg-adaptive-routing-arn-03, 13
September 2024, <https://datatracker.ietf.org/doc/html/
draft-wh-rtgwg-adaptive-routing-arn-03>.
[I-D.liu-rtgwg-adaptive-routing-notification]
Liu, Y., lihesong, and W. Duan, "Adaptive Routing
Notification for Load-balancing", Work in Progress,
Internet-Draft, draft-liu-rtgwg-adaptive-routing-
notification-01, 20 October 2024,
<https://datatracker.ietf.org/doc/html/draft-liu-rtgwg-
adaptive-routing-notification-01>.
[I-D.zzhang-rtgwg-router-info]
Zhang, Z. J., Wang, K., Lin, C., and N. Vaidya,
"Advertising Router Information", Work in Progress,
Internet-Draft, draft-zzhang-rtgwg-router-info-01, 18
September 2024, <https://datatracker.ietf.org/doc/html/
draft-zzhang-rtgwg-router-info-01>.
Authors' Addresses
Jie Dong
Huawei Technologies
No. 156 Beiqing Road
Beijing
China
Email: jie.dong@huawei.com
Dan Li
Tsinghua University
Beijing
China
Email: tolidan@tsinghua.edu.cn
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Qinru Shi
Huawei Technologies
No. 156 Beiqing Road
Beijing
China
Email: shiqinru@huawei.com
PengFei Huo
ByteDance
Beijing
China
Email: huopengfei@bytedance.com
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